The following relates to the transportation arts, tracking arts, data processing and data management arts.
Intelligent transportation systems may use real time information to assist public transport agencies in decision making. Such intelligent transportation systems may incorporate automatic data collection subsystems so as to monitor vehicle ridership along a route and allow for the periodic adjustment of headway plans, in order to improve the quality of service and to make the transportation system more attractive to the travelers.
Previous attempts to monitor and modify transportation systems made use of expensive and time-consuming procedures, such as household surveys and roadside interviews. Intelligent transportation systems, in contrast, allow the incorporation of sensor- and detector-based technologies, which enable the collection of large amounts of traveler information in real-time, which may then be processed to facilitate more efficient planning by associated transportation system personnel. Urban public transportation networks often deploy automatic ticketing validation systems, which can serve as sensors.
A public transport trip planner, or journey planner, may be designed to provide information about available public transport journeys or routes along the public transportation system, for example via a Web-based application. Such an application may prompt a prospective traveler to input an origin and a destination, and then use a trip planning engine to determine a route between the two input locations using specified available public transportation services and routes, e.g., buses, trams, trains, etc., depending on available schedules for these services. Accordingly, transportation authorities may include such a public transport journey planner on their websites, London for example, has a multi-modal journey planner covering all modes of public transport in London, including bus, tube and rail, as described more fully in “Mining public transport usage for personalised intelligent transport systems,” Neal Lathia, Jon Froehlich, and Licia Capra, ICDM, pages 887-892, 2010. A similar trip planner with all modes included is maintained by the Helsinki Metropolitan Area Council in Finland, as described more fully in “An examination of the public transport information requirements of users,” Brian Caulfield and Margaret O'Mahony, IEEE Transactions on Intelligent Transportation Systems, 8(1):21-30, 2007.
A trip planner may find one or more suggested paths between an origin and a destination. The origin and destination may be specified as geospatial coordinates or names of points of access to public transport such as bus stops, stations, airports or ferry ports. A location finding process may resolve the origin and destination into the nearest known nodes on the transport network in order to compute a trip plan over its data set of known paths, i.e., routes. Trip planners for large networks may use a search algorithm to search a graph of nodes (representing access points to the transport network) and edges (representing possible journeys between points). Different weightings such as distance, cost or accessibility may be associated with each edge. When suggesting the journey or trip plans, the trip planner uses the abstract model of the public transportation network and those plans may not reflect the trips that travelers make every day on these routes. Accordingly, traffic jams, traveler congestion, delays, crowding, and the like, are not taken into account when determining the routes provided by the trip planner.
Thus, it would be advantageous to provide a method and system which reflect traveler patterns of behavior for trip planning.